Modeling and artificial intelligence

Abstract The field of artificial intelligence ( AI) is concerned with improving the understanding of the nature of concepts such as knowledge, intelligence, and consciousness. In the development of the field, modeling the environment has emerged as a central task. Modeling in this sense is a broad term embracing three evolutionary levels: an early stage of biochemical structures, a middle stage in which neural systems emerge, and the later conceptual stage characterized by the use of symbolic structures such as language, interpreted images, and signs. Although AI is particularly concerned with specific area of symbolic knowledge processing, that of logical and relational knowledge, central efforts in this field are also directed at integrating all forms of symbolic modeling into effective solutions for knowledge processing on computers. Thus, methods from computer science are of major importance. This paper illustrates the integration of various forms of modeling within the field of AI. It also shows how ...

[1]  Franz-J. Radermacher,et al.  Model Management: The Core of Intelligent Decision Support , 1990 .

[2]  Lothar Späth.  Wende in die Zukunft : die Bundesrepublik auf dem Weg in die Informationsgesellschaft , 1985 .

[3]  H. A. Mallot,et al.  Parallelism and redundancy in neural networks , 1989 .

[4]  Michael M. Richter.  Prinzipien der Künstlichen Intelligenz , 1992 .

[5]  Matthias Jarke,et al.  The AI potential of model management and its central role in decision support , 1988, Decis. Support Syst..

[6]  Aaron Sloman,et al.  Motives, Mechanisms, and Emotions , 1987, The Philosophy of Artificial Intelligence.

[7]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  T. Kuhn.  The Structure of Scientific Revolutions. , 1964 .

[9]  Peter Bock.  A parallel implementation of collective learning systems theory: Adaptive Learning Image Analysis System (ALIAS) , 1990, CSC '90.

[10]  Daniel C. Dennett,et al.  The Mind's I , 1982 .

[11]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[12]  K. Popper.  Objective Knowledge: An Evolutionary Approach , 1972 .

[13]  J. Siskos.  Assessing a set of additive utility functions for multicriteria decision-making , 1982 .

[14]  Robin Gandy,et al.  Church's Thesis and Principles for Mechanisms , 1980 .

[15]  Wilfried Brauer,et al.  Grenzen machineller Berechenbarkeit , 1990, Inform. Spektrum.

[16]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[17]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[18]  Terry Winograd,et al.  Understanding computers and cognition - a new foundation for design , 1987 .

[19]  Jürg Kohlas.  Conditional Belief Structures , 1988 .

[20]  B. Fischhoff,et al.  KNOWING WHAT YOU WANT: MEASURING LABILE VALUES , 1988 .

[21]  J. Neumann,et al.  Theory of games and economic behavior , 1945, 100 Years of Math Milestones.

[22]  Jay W. Cohen,et al.  Structured modeling , 1982, Winter Simulation Conference.

[23]  Franz Josef Radermacher,et al.  Freiheit und Bindung menschlicher Entscheidungen , 1990 .

[24]  R. Eckmiller,et al.  Neural Computers , 1989, Springer Study Edition.

[25]  Hermann Haken,et al.  Synergetics: An Introduction , 1983 .

[26]  Ralf Felter.  Decision Support Assistant , 1989 .

[27]  Peter C. Fishburn,et al.  Utility theory for decision making , 1970 .

[28]  Hans Kamp,et al.  Discourse Representation Theory: What it is and Where it Ought to Go , 1988, Natural Language at the Computer.

[29]  Ramanathan V. Guha,et al.  Building large knowledge-based systems , 1989 .

[30]  Donald A. Waterman,et al.  A Guide to Expert Systems , 1986 .

[31]  Rolf H. Möhring,et al.  Design aspects of advanced decision support systems , 1988, Decision Support Systems.